As Information Technology (IT) continues to advance, both business offerings and customers/clients needs continue to grow. In the field of service delivery, it becomes a challenge to provide to a customer a solution in a most effective time to market. The on-demand era implies the need to generate accurate offering quotes whatever the variation of the customer requirements. To address such need, a solution provider has to be sure that the service commitments meet guidelines to facilitate the business quotation.
The client's needs for cost flexibility generate today a strong expectation to purchase facilities to satisfy the on-demand terms and conditions that are initiated by the business. And clients are essentially looking for services where all the resources (Hardware, Software, People, . . . ) are provided and in place on time.
From the service provider perspective, the issues to manage are usually to:                design and deploy a solution that includes flexible and scalable facilities (or service component); and        estimate the selling price for these facilities with an associated level of risk.        
Managing the cost associated to a service component with a corresponding price unit depends directly on the business context and depending on cost variation, it may generate several complex quotation iterations for the service provider. The usual way to proceed depends on the relationship existing between the cost and the price unit and how they interact.
On one hand, when the variation of the costs element is proportional to the variation of the price unit, the final quotation may be updated quite quickly.
On the other hand, when there is not real coherence between the cost and the price, any variation on one or the other item has an impact on the existing quotation. Different parameters such as investments (in Hardware/Software) and skill resources availability have to deal with the complexity of running numerous iterations to fix the quotation in real time. A current quotation needs then to be recalculated taking into account the various parameters in order to generate an updated price unit and an associated invoice.
For example, let assume that a service provider wants to sell a storage infrastructure composed of backup servers, Storage Area Network (SAN), tape libraries, off-site warehousing for tapes, disk bays, and so as well as selling an end-to-end management. To size the price of the overall solution, a “per Giga Bytes (GB)” price is adopted. However, because individual costs are not each proportional to the volume of GB deployed (e.g. at least one backup server is to be deployed right from the beginning, even for a single GB sold), it becomes difficult to estimate the impact on the price per GB of an architectural change (such as doubling the SAN for example). It is also difficult to anticipate what will be the volume of GB that the customer will really deploy and the pace of that deployment.
Thus, the main challenge resides in defining an “end-to-end” solution, from the architecture design up to the selling price, while being able to measure quickly the impact on cost, profitability, ROI, etc of different architecture or deployment assumptions.
The existing pricing methods consist in:                1. Specifying the assumptions (the non functional requirements, the volumes, the ramp-up or ramp-down, etc);        2. Designing a solution, often based on the target volumes and requirements;        3. Costing the solution, with an estimated deployment pace of the components; and        4. Computing a price by setting price units and computing a revenue (or cost recovery) using the deployment pace assumed; and        5. Generating all the financial (profitability, ROI, payback, etc).        
This approach requires that both the deployment of the technical components and the revenue corresponding to the invoiced work units be synchronized together. Then, to simulate and validate additional scenarios (by changing assumptions like the deployment pace, the minimum volume commitments, the work units to invoice, the architecture or the components), it is necessary to restart the entire quotation process by including all the new assumptions plus the collateral modifications such as the skilled resources involved like architects, specialists, finance people, etc. . . .
Moreover, it is a reality that during engagements the client's expectations are changing and evolving very quickly, which implies to align up the infrastructure solution and obviously the associated price at the same rate. This becomes even more complex for the on-demand solutions.
Then, the standard quotation solutions depends on too many manual operations (e.g. entering a volume quantities month by month or quarter by quarter in the architecture models, in the cost models, in the price models). This raises a difficulty to get quickly all the necessary information to be used for the assessment of the quotation, and which is thus a source of errors.
To speed up the assessment process of the standard solution, one way is to use some predefined shortcuts that are available to compute an average cost per work unit. However, it is a rigid approach that is clearly not compliant with the on-demand concept, which aim is to allow both the provider and the client to quickly and safely re-adjust the solution in real-time by minimizing the financial impact for the two parties.
In addition, the existing solutions do not exactly consider the appropriate level of risk associated with the investment peaks and can financially expose the service provider. To mitigate this risk, some improvements to the standard solutions consist in computing an average cost at 50% capacity of the infrastructure. Applying such assumption provides the user with some incoherent results that do not match with the business expectation, do not provide the appropriate level of risk and do not reflect the business reality. Moreover, the standard solution is not adjustable to allow an assessment of the quotation corresponding to different business perspectives.
This is why the architects need to estimate the impact of the changes made to the solution with great care, and in particular, the risk associated with the client's budget and the level of risk taken by the service provider to satisfy the on demand solution requirements.
To summarize, the aforementioned methods present several drawbacks, some of the main ones are:                The standard method is too rigid to be successfully exercised all along an adjustable quotation process when both volume/quantities and price vary.        The standard method depends on too many manual operations that offer room for errors on business quotation.        The standard method is unaware of the business investment peaks and an incorrect level of risk may be generated when the quotation needs to be redone.        The standard method is not flexible to be applied for different business perspectives and provide the adequate quotation.        The standard method is based on a slow moving process that needs to include all the resources involved to generate an updated business quotation.        
Then, there is a need for a quotation method that solve the aforementioned problems. The present invention offers such solution.